SoraWatermarkCleaner/sorawm/iopaint/plugins/facexlib/parsing/resnet.py

import torch.nn as nn
import torch.nn.functional as F


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(
        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
    )


class BasicBlock(nn.Module):
    def __init__(self, in_chan, out_chan, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(in_chan, out_chan, stride)
        self.bn1 = nn.BatchNorm2d(out_chan)
        self.conv2 = conv3x3(out_chan, out_chan)
        self.bn2 = nn.BatchNorm2d(out_chan)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = None
        if in_chan != out_chan or stride != 1:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_chan),
            )

    def forward(self, x):
        residual = self.conv1(x)
        residual = F.relu(self.bn1(residual))
        residual = self.conv2(residual)
        residual = self.bn2(residual)

        shortcut = x
        if self.downsample is not None:
            shortcut = self.downsample(x)

        out = shortcut + residual
        out = self.relu(out)
        return out


def create_layer_basic(in_chan, out_chan, bnum, stride=1):
    layers = [BasicBlock(in_chan, out_chan, stride=stride)]
    for i in range(bnum - 1):
        layers.append(BasicBlock(out_chan, out_chan, stride=1))
    return nn.Sequential(*layers)


class ResNet18(nn.Module):
    def __init__(self):
        super(ResNet18, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
        self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
        self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
        self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)

    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(self.bn1(x))
        x = self.maxpool(x)

        x = self.layer1(x)
        feat8 = self.layer2(x)  # 1/8
        feat16 = self.layer3(feat8)  # 1/16
        feat32 = self.layer4(feat16)  # 1/32
        return feat8, feat16, feat32